A Deep Learning based Fast Signed Distance Map Generation

TL;DR

This paper introduces a deep learning neural network that rapidly generates signed distance maps for 3D shapes, significantly reducing computation time while maintaining accuracy, especially demonstrated on a cochlea shape model.

Contribution

The paper presents a novel neural network approach for fast SDM generation, achieving a 60-fold speedup over traditional methods for 3D shape representations.

Findings

60-fold reduction in computation time

Effective generation of SDMs for cochlea shape model

Good balance between accuracy and efficiency

Abstract

Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters. The proposed SDM Neural Network generates a cochlea signed distance map depending on four input parameters and we show that the deep learning approach leads to a 60 fold improvement in the time of computation compared to more classical SDM generation methods. Therefore, the proposed approach achieves a good trade-off between accuracy and efficiency.